Sports fans, sports players, and sports management are looking beyond watching a game and reviewing the post-game data generated by human observation. The locations of several participant(s) or player(s) in a “fast-moving” game such as ice hockey dynamically changes; making the constant recording of player locations by human observation unfeasible. Video, comprised of a series of image frames, produced from a game can be digitized and processed as electronic images to generate additional data beyond the capability of human observation. The predefined area of play such as an ice hockey rink, can be thought of as an XY Cartesian plane with user-defined coordinates suitable for recording player locations. Video, often collected for watching by a wide audience, does more than simply offer a view of the game. Video is a “data-ready” technology that depicts players and player locations at different scales, from vertical to oblique angles, suitable for extracting the identification of players and registering player locations digitally to a predefined XY Cartesian plane covering the field of play.
The use of spatial analysis to identify spatial patterns and make predictions has moved beyond the use of analog data such as: photographs or human observation. Spatial analysis now utilizes digitally acquired data that can be directly input into a computer system for processing. To improve the spatial analysis of sports participants to identify patterns and make predictions requires volumes of data beyond the capability of analog recording by human observations. Digital player identification data and digital player location data, extracted from video image frames, can be implemented in a system to process metrics for post-game analysis and reporting. There is therefore, a need for an improved method of and system for such computer-based processing large volumes of video data to detect, analyze, and report on players in sporting events that goes beyond human observation.